diff --git a/examples/evaluate_model.py b/examples/evaluate_model.py
index e74601df..3a902527 100644
--- a/examples/evaluate_model.py
+++ b/examples/evaluate_model.py
@@ -65,11 +65,25 @@ def parse_args() -> argparse.Namespace:
         help="JSON file to dump raw source / target texts to.",
         default="model_preds.json",
     )
+    parser.add_argument(
+        "--max_new_tokens",
+        help="Maximum number of new tokens to be generated",
+        type=int,
+        default=20,
+    )
+    parser.add_argument(
+        "--truncate_input_tokens",
+        help="Number of allowed input tokens (no truncation=0)",
+        type=int,
+        default=0,
+    )
     args = parser.parse_args()
     return args
 
 
-def get_model_preds_and_references(model, validation_stream):
+def get_model_preds_and_references(
+    model, validation_stream, max_new_tokens, truncate_input_tokens
+):
     """Given a model & a validation stream, run the model against every example in the validation
     stream and compare the outputs to the target/output sequence.
 
@@ -79,6 +93,10 @@ def get_model_preds_and_references(model, validation_stream):
         validation_stream: DataStream[GenerationTrainRecord]
             Validation stream with labeled targets that we want to compare to our model's
             predictions.
+        max_new_tokens: int
+            Max number of new tokens to be generated, i.e., output limit
+        truncate_input_tokens: int
+            Number of allowed input tokens, i.e., input limit
 
     Returns:
         Tuple(List)
@@ -90,7 +108,11 @@ def get_model_preds_and_references(model, validation_stream):
     for datum in tqdm(validation_stream):
         # Local .run() currently prepends the input text to the generated string;
         # Ensure that we're just splitting the first predicted token & beyond.
-        raw_model_text = model.run(datum.input).generated_text
+        raw_model_text = model.run(
+            datum.input,
+            max_new_tokens=max_new_tokens,
+            truncate_input_tokens=truncate_input_tokens,
+        ).generated_text
         parse_pred_text = raw_model_text.split(datum.input)[-1].strip()
         model_preds.append(parse_pred_text)
         targets.append(datum.output)
@@ -153,7 +175,9 @@ def export_model_preds(preds_file, predictions, validation_stream, verbalizer):
 
     # Run the data through the model; save the predictions & references
     print_colored("Getting model predictions...")
-    predictions, references = get_model_preds_and_references(model, validation_stream)
+    predictions, references = get_model_preds_and_references(
+        model, validation_stream, args.max_new_tokens, args.truncate_input_tokens
+    )
     print_colored(
         "Exporting model preds, source, verbalized source, and ground truth targets to {}".format(
             args.preds_file